In modern textile industrial processes, fast and efficient detection of textile defects plays a crucial role in textile quality control. Recently, as a critical machine-learning method, faster region-based convolutional neural network (Faster RCNN) have arisen as a promising framework, providing competitive performance for object detection. However, detecting small-scale objects, such as micro-defects on textile, is still a challenging task for Faster RCNN. To address the challenge, this paper aims to develop a new detection model to improve the ability of detecting small-scale objects. First, by analyzing the relationship between the attention mechanism and the visual gain mechanism, we find that the attention-related visual gain mechanism can modify response amplitude without changing selectivity and improve the acuity of visual perception. Then, the relevant mechanisms are further incorporated into the Faster RCNN model to build a new model called Faster VG-RCNN. To evaluate the proposed detection model, a unique textile micro-defect database is built as the benchmark for micro-defect detection. Furthermore, we conduct extensive experimental validations for various design choices. The experimental results show that the proposed Faster VG-RCNN outperforms the existing detection methods. In particular, compared to Faster RCNN, Faster VG-RCNN improves the detection precision from 90.1% to 94.3%.

在现代纺织工业过程中，快速有效地检测纺织品缺陷在纺织品质量控制中起着至关重要的作用。近年来，作为一种重要的机器学习方法，基于区域的更快卷积神经网络（Faster RCNN）已成为一种很有前途的框架，为目标检测提供了有竞争力的性能。但是，对于Faster RCNN而言，检测诸如纺织品上的微瑕疵之类的小规模物体仍然是一项艰巨的任务。为了应对这一挑战，本文旨在开发一种新的检测模型，以提高对小规模物体的检测能力。首先，通过分析注意力机制和视觉增益机制之间的关系，我们发现与注意力有关的视觉增益机制可以在不改变选择性的情况下改变响应幅度，并提高视觉感知的敏锐度。然后，相关机制已进一步纳入Faster RCNN模型中，以建立称为Faster VG-RCNN的新模型。为了评估提出的检测模型，建立了一个独特的纺织品微缺陷数据库作为微缺陷检测的基准。此外，我们针对各种设计选择进行了广泛的实验验证。实验结果表明，所提出的Faster VG-RCNN优于现有的检测方法。特别是，与Faster RCNN相比，Faster VG-RCNN将检测精度从90.1％提高到94.3％。我们针对各种设计选择进行了广泛的实验验证。实验结果表明，所提出的Faster VG-RCNN优于现有的检测方法。特别是，与Faster RCNN相比，Faster VG-RCNN将检测精度从90.1％提高到94.3％。我们针对各种设计选择进行了广泛的实验验证。实验结果表明，所提出的Faster VG-RCNN优于现有的检测方法。特别是，与Faster RCNN相比，Faster VG-RCNN将检测精度从90.1％提高到94.3％。